- Context window management – Techniques for fitting optimal context without exceeding token limits or degrading attention.
- Advanced concepts (less common): Sparse retrieval models like SPLADE, hypothetical document embeddings (HyDE), and graph-based RAG topologies.
Example questions or scenarios:
- "How would you design a RAG pipeline that handles constantly updating company expense policies with zero stale data leakage?"
- "Explain how you would debug a scenario where your vector search retrieves high semantic similarity matches that contain factually incorrect answers for financial auditing."
Embeddings and Vector Search
Understanding how embeddings capture semantic relationships and how vector databases operate at scale is critical for this role. You will be tested on your ability to select appropriate embedding models, optimize vector index configurations, and manage latency-recall tradeoffs in production environments.
Be ready to go over:
- Vector database internals – Indexing algorithms like HNSW and IVF, and how memory mapping affects retrieval speed.
- Embedding fine-tuning – When and how to fine-tune embedding models on domain-specific financial text.
- Dimensionality reduction – Techniques like PCA or Matryoshka embeddings to reduce storage and network overhead.
- Advanced concepts (less common): Product quantization, approximate nearest neighbor edge cases, and multi-modal embedding alignment.
Example questions or scenarios:
- "You notice your vector search query latency spiking as your document store grows to 100 million vectors. How do you diagnose and resolve the bottleneck?"
- "How do you choose between an open-source embedding model hosted internally versus a commercial API?"
Multi-Agent Systems and Orchestration
As AI applications evolve from simple chatbots to autonomous workers, multi-agent architectures become essential for complex financial workflows like procurement and automated reconciliation. Interviewers will assess your ability to design robust agent loops, handle tool execution failures, and maintain state across multi-step reasoning tasks.
Be ready to go over:
- Agentic workflows – Implementing ReAct loops, planner-executor patterns, and reflection mechanisms.
- Tool calling and error handling – Managing malformed tool outputs, infinite recursion loops, and deterministic fallback paths.
- State management – Persisting agent state across asynchronous jobs and distributed microservices.
- Advanced concepts (less common): Multi-agent consensus protocols, hierarchical agent delegation, and dynamic prompt routing based on task complexity.
Example questions or scenarios:
- "Design a multi-agent system that reviews corporate card receipts against company travel policy and flags anomalies for human review."
- "How do you prevent a reasoning agent from entering an infinite loop of failed tool calls?"
LLM Evaluation and Guardrails
Deploying AI in a fintech environment leaves zero room for silent failures, hallucinated numbers, or policy violations. You must know how to build comprehensive evaluation harnesses and robust runtime guardrails to ensure deterministic behavior.
Be ready to go over:
- Evaluation frameworks – Implementing LLM-as-a-judge patterns, BLEU/ROUGE limitations, and task-specific regression testing.
- Guardrails and safety – Input sanitization, PII redaction, output schema validation, and prompt injection defense.
- Deterministic extraction – Techniques like constrained decoding to guarantee strict JSON schema compliance.
- Advanced concepts (less common): Adversarial red-teaming automation, confidence calibration scoring, and shadow deployment monitoring.
Example questions or scenarios:
- "How would you set up a CI/CD pipeline that automatically evaluates a new model version for regression before promoting it to production?"
- "A user attempts a prompt injection attack to bypass spending limits via the chat interface. How do your guardrails catch and neutralize it?"
System Design for LLM Serving
Serving large language models at production scale requires deep familiarity with inference infrastructure, throughput optimization, and hardware constraints. Interviewers want to see that you understand how to balance cost, latency, and concurrency.
Be ready to go over:
- Inference engines – Optimizations using vLLM, TensorRT-LLM, continuous batching, and PagedAttention.
- Caching layers – Semantic caching and prompt prefix caching to reduce redundant compute.
- Load balancing and autoscaling – Managing GPU cluster scaling based on queue depth and token generation rates.
- Advanced concepts (less common): Quantization formats (GPTQ, AWQ, FP8), speculative decoding, and multi-tenant resource isolation.
Example questions or scenarios:
- "Design an LLM serving infrastructure that handles unpredictable traffic bursts from 50,000 active corporate users with sub-second time-to-first-token."
- "How do you decide when to self-host an open-weight model versus routing traffic to a managed cloud LLM provider?"
